Abstract

An optimistic learning bias leads people to update their beliefs in response to better-than-expected good news but neglect worse-than-expected bad news. Because evidence suggests that this bias arises from self-concern, we hypothesized that a similar bias may affect beliefs about other people's futures, to the extent that people care about others. Here, we demonstrated the phenomenon of vicariousoptimism and showed that it arises from concern for others. Participants predicted the likelihood of unpleasant future events that could happen to either themselves or others. In addition to showing an optimistic learning bias for events affecting themselves, people showed vicariousoptimism when learning about events affecting friends and strangers. Vicariousoptimism for strangers correlated with generosity toward strangers, and experimentally increasing concern for strangers amplified vicariousoptimism for them. These findings suggest that concern for others can bias beliefs about their future welfare and that optimism in learning is not restricted to oneself.

Vicariousoptimism task. On each trial (a), participants imagined a negative event happening to a target individual (friend or stranger), estimated the likelihood of the event happening to the target, learned about the average likelihood for that event, and finally reestimated the likelihood. A good-news event (b) was defined by a first estimate that was higher than the average likelihood. The estimation error was then calculated by subtracting the first estimate from the average likelihood, and the update was calculated by subtracting the first estimate from the second estimate. The learning rate, which indicated how well the estimation error predicted the subsequent update, was the unstandardized regression coefficient indicating the strength of the relationship between the estimation error and the subsequent update. A bad-news event (c) was defined by a first estimate that was lower than the average likelihood. The estimation error was then calculated by subtracting the average likelihood from the first estimate, and the update was calculated by subtracting the second from the first estimate. Again, the learning rate indicated how well the estimation error predicted the subsequent update.

Results from Study 1 (N = 68): mean learning rate as a function of whether participants received good news versus bad news, separately for the self and friend conditions. The learning rate is the unstandardized regression coefficient indicating the strength of the relationship between the estimation error and the subsequent update. Error bars represent standard errors of the mean. Asterisks indicate significant differences between conditions (p < .05).